基础模型与可视化的碰撞：挑战与机遇
Foundation Models Meet Visualizations: Challenges and Opportunities
AI总结：
本文梳理了基础模型与可视化交叉的两大研究方向VIS4FM和FM4VIS的研究内容，分析了二者融合面临的挑战与发展机遇，为该领域后续研究提供参考起点。
摘要：
近期研究表明，BERT、GPT等基础模型在适配各类下游任务方面表现出色，这种适配性使其成为构建人工智能（AI）系统的核心力量。当可视化技术与这些模型交叉融合时，一种新的研究范式应运而生。本文将这些交叉领域划分为两大方向：面向基础模型的可视化（VIS4FM）和面向可视化的基础模型（FM4VIS）。在VIS4FM方向，我们探究了可视化在理解、优化与评估这些复杂模型中的核心作用，以满足当前对透明度、可解释性、公平性与鲁棒性的迫切需求。而在FM4VIS方向，我们重点阐述了如何利用基础模型推动可视化领域自身的发展。基础模型与可视化的融合前景广阔，但也伴随着一系列挑战。本文通过梳理这些挑战与日益增长的机遇，旨在为这一极具潜力的研究方向的持续探索提供起点。
英文摘要：
Recent studies have indicated that foundation models, such as BERT and GPT, excel in adapting to a variety of downstream tasks. This adaptability has established them as the dominant force in building artificial intelligence (AI) systems. As visualization techniques intersect with these models, a new research paradigm emerges. This paper divides these intersections into two main areas: visualizations for foundation models (VIS4FM) and foundation models for visualizations (FM4VIS). In VIS4FM, we explore the primary role of visualizations in understanding, refining, and evaluating these intricate models. This addresses the pressing need for transparency, explainability, fairness, and robustness. Conversely, within FM4VIS, we highlight how foundation models can be utilized to advance the visualization field itself. The confluence of foundation models and visualizations holds great promise, but it also comes with its own set of challenges. By highlighting these challenges and the growing opportunities, this paper seeks to provide a starting point for continued exploration in this promising avenue.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
